The dynamic diversity and invariance of ab-initio water

Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water’s structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob’s ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and η adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate liquid water’s dynamic properties. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path toward designing better functionals for water but also underscore the significance of water’s many-body characteristics.

在海水淡化、离子分离、电催化以及生物化学过程等众多领域，理解水动力学至关重要。尽管从头算分子动力学（AIMD）能够精确描绘水的结构，但在纳秒时间尺度上计算其动态性质却因计算成本过高而变得不可行。本研究采用机器学习势（MLP）以从头算精度准确确定液态水的动态性质。我们的研究结果表明，在不同方法中计算出的扩散系数（D）和水（η）的粘度存在多样性。具体而言，虽然广义梯度近似（GGA）、元广义梯度近似（meta-GGA）以及混合泛函方法在预测环境条件下的动态性质方面存在困难，但处于DFT近似雅可比梯级更高层次的方法则表现出显著优越性。令人好奇的是，我们发现所有D和η均遵循已建立的斯托克斯-爱因斯坦（SE）关系，适用于所有从头算水。不同方法间观察到的多样性可归因于独特的结构熵，证实了过剩熵标度关系适用于所有泛函。D和η之间的相关性为识别准确复制液态水动态性质的理想温度提供了宝贵的见解。此外，我们的研究结果验证了在AIMD模拟中采用人工高温度的合理性。这些成果不仅为设计更优的水泛函铺平了道路，也凸显了水多体特性的重要性。